Google: Gemma 4 26B A4B

Access Gemma 4 26B A4B from Google using Puter.js AI API.

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Model Card

Gemma 4 26B A4B is a Mixture-of-Experts (MoE) open model from Google DeepMind, built from the same research as Gemini 3. It has 26B total parameters but activates only 3.8B per forward pass, delivering near-31B-dense quality at a fraction of the compute cost.

The model supports a 256K token context window, multimodal image and text input, built-in step-by-step reasoning (thinking mode), and native function calling for agentic workflows. It currently ranks #6 among open models on the Arena AI text leaderboard with an estimated LMArena score of 1441 — competitive with models many times its active size.

It excels at reasoning, coding, long-context tasks, and structured tool use. It's a strong pick for developers who need high throughput and low latency without sacrificing capability.

Context Window 262K

tokens

Max Output 262K

tokens

Input Cost $0.13

per million tokens

Output Cost $0.4

per million tokens

Release Date Apr 3, 2026

 

API Usage Example

Add Gemma 4 26B A4B to your app with just a few lines of code.
No backend, no configuration required.

// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';

puter.ai.chat("Explain quantum computing in simple terms", {
    model: "google/gemma-4-26b-a4b-it"
}).then(response => {
    document.body.innerHTML = response.message.content;
});

<html>
<body>
    <script src="https://js.puter.com/v2/"></script>
    <script>
        puter.ai.chat("Explain quantum computing in simple terms", {
            model: "google/gemma-4-26b-a4b-it"
        }).then(response => {
            document.body.innerHTML = response.message.content;
        });
    </script>
</body>
</html>

# pip install openai
from openai import OpenAI

client = OpenAI(
    base_url="https://api.puter.com/puterai/openai/v1/",
    api_key="YOUR_PUTER_AUTH_TOKEN",
)

response = client.chat.completions.create(
    model="google/gemma-4-26b-a4b-it",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms"}
    ],
)

print(response.choices[0].message.content)

curl https://api.puter.com/puterai/openai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
  -d '{
    "model": "google/gemma-4-26b-a4b-it",
    "messages": [
      {"role": "user", "content": "Explain quantum computing in simple terms"}
    ]
  }'

View full documentation →

More AI Models From Google

Chat

Gemma 4 31B

Gemma 4 31B is a dense multimodal model from Google DeepMind, built on the same research foundation as Gemini 3. It is the most capable model in the Gemma 4 family, accepting text, image, and video input with a 256K-token context window. It delivers strong benchmark results: 89.2% on AIME 2026, 85.2% on MMLU Pro, 80.0% on LiveCodeBench v6, and 84.3% on GPQA Diamond. On the Arena AI text leaderboard, it ranks as the #3 open model globally, outperforming many models with far higher parameter counts. Gemma 4 31B features native function calling trained into the model, configurable chain-of-thought reasoning, and structured JSON output — making it especially well-suited for agentic workflows, coding tasks, and multi-turn tool use. It supports over 140 languages and serves as a strong foundation for fine-tuning.

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Gemini 3.1 Flash Lite Preview

Gemini 3.1 Flash Lite is Google's fastest and most cost-efficient model in the Gemini 3 series, optimized for high-volume, latency-sensitive tasks like translation, classification, and content moderation. Priced at $0.25/1M input tokens and $1.50/1M output tokens, it outperforms Gemini 2.5 Flash with 2.5x faster time-to-first-token and a 45% boost in output speed.

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Gemini 3.1 Flash Image

Gemini 3.1 Flash Image (also known as Nano Banana 2) is Google DeepMind's latest state-of-the-art image generation and editing model, combining Pro-level quality with the speed of the Flash architecture. It supports text and image input with up to 1M token context, generates images up to 4K resolution, and features advanced world knowledge, precise text rendering, subject consistency, and web-search grounding.

View all Google models →

Frequently Asked Questions

How do I use Gemma 4 26B A4B?

You can access Gemma 4 26B A4B by Google through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.

Is Gemma 4 26B A4B free?

Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Gemma 4 26B A4B to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.

What is the pricing for Gemma 4 26B A4B?
Pricing for Gemma 4 26B A4B is based on the number of input and output tokens used per request.
Price per 1M tokens
Input$0.13
Output$0.4
Who created Gemma 4 26B A4B?

Gemma 4 26B A4B was created by Google and released on Apr 3, 2026.

What is the context window of Gemma 4 26B A4B?

Gemma 4 26B A4B supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.

What is the max output length of Gemma 4 26B A4B?

Gemma 4 26B A4B can generate up to 262K tokens in a single response.

Does it work with React / Vue / Vanilla JS / Node / etc.?

Yes — the Gemma 4 26B A4B API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.

Get started with Puter.js

Add Gemma 4 26B A4B to your app without worrying about API keys or setup.

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